A Notch signaling-related lncRNA signature for predicting prognosis and therapeutic response in clear cell renal cell carcinoma

Increasing evidence has confirmed the vital role of Notch signaling in the tumorigenesis of clear cell renal cell carcinoma (ccRCC). The underlying function of long non-coding RNA (lncRNA) related to Notch signaling in ccRCC remains unclear. In present study, the prognostic value and therapeutic strategy of Notch signaling-related lncRNA are comprehensively explored in ccRCC. In total, we acquired 1422 NSRlncRNAs, of which 41 lncRNAs were identified the key NSRlncRNAs associated with the occurrence of ccRCC. The prognostic signature containing five NSRlncRNAs (AC092611.2, NNT-AS1, AGAP2-AS1, AC147651.3, and AC007406.3) was established and validated, and the ccRCC patients were clustered into the high- and low-risk groups. The overall survival of patients in the low-risk group were much more favorable than those in the high-risk group. Multivariate Cox regression analysis indicated that the risk score was an independent prognostic biomarker. Based on the risk score and clinical variables, a nomogram for predicting prognosis of ccRCC patients was constructed, and the calibration curves and DCA curves showed the superior predictive ability of nomogram. The risk score was correlated with immune cell infiltration, targeted therapy or chemotherapy sensitivity, and multiple oncogenic pathways. Additionally, consensus clustering analysis stratified the ccRCC patients into four clusters with obvious different outcomes, immune microenvironments, and expression of immune checkpoints. The constructed NSRlncRNA-based signature might serve as a potential biomarker for predicting prognosis and response to immunotherapy or targeted therapy in patients with ccRCC.


Acquisition of the Notch signaling-related lncRNA
We downloaded the transcriptional expression profile as fragments per kilobase of transcript per million mapped reads (FPKM), clinicopathological characteristics, and survival-related data for patients with clear cell renal cell cancer (ccRCC) from the Cancer Genome Atlas database (TCGA, https:// portal.gdc.cancer.gov/).Totally, we have acquired the RNA sequencing data of 611 samples (539 ccRCC samples and 72 normal renal samples) and the corresponding clinicopathological variables including age, AJCC stage, gender, and grade.The lncRNA annotation data of GRCh38 was provided by the GENCODE database (https:// www.genco degen es.org).The 32 Notch pathway-related genes were obtained from the "HALLMARK_NOTCH_SIGNALING" gene set of the Molecular Signatures Database version 7.5 (MSigDB, https:// www.gsea-msigdb.org/ GSEA/ misgdb).In addition, the correlation between Notch signaling genes and Notch signaling-related lncRNAs (NSRlncRNAs) was weighed by Pearson correlation analysis, and the NSRlncRNAs were identified with the criterion that the correlation coefficient (| R2 |> 0.4) and p < 0.001.The overall workflow of the current study was depicted in Fig. 1.

Identification of the key Notch signaling-related lncRNAs
Weighted gene co-expression network analysis (WGCNA), as a crucial bioinformatics approach, was conducted to investigate the NSRlncRNAs between various samples and identify lncRNA modules notably correlated with carcinogenesis by using R package "WGCNA".To screen the most cancer-associated modules, we performed the module-trait correlation analyses for each module.Subsequently, the NSRlncRNAs from the crucial cancercorrelated modules were used for further analysis.In addition, the differentially expressed NSRlncRNAs (DEN-SRlncRNAs) between ccRCC tissues and normal renal tissues was identified through the application of R package "edgeR" with the criterion of false discovery rate (FDR) < 0.05 and |log fold change (FC) |> 1.Finally, Draw Venn Diagram (http:// bioin forma tics.psb.ugent.be/ webto ols/ Venn/), as an online website, was conducted to acquire the overlapping lncRNAs and identify the key NSRlncRNAs.

Construction and verification of Notch signaling-related lncRNA signature
Patients with ccRCC in the total dataset (n = 530) were randomly classified into the training dataset (n = 266) or testing dataset (n = 264) with a ratio 1:1.The training dataset was applied to establish the prognostic signature, while the testing dataset and total dataset were used to validate the constructed signature.The univariate Cox regression analysis was applied to identify NSRlncRNA correlated with prognosis (p < 0.05), which were subsequently incorporated for the least absolute shrinkage and selection operator (LASSO) regression analysis, followed by the establishment of the Notch-related lncRNA-based risk signature.The risk score of each ccRCC patient was generated via the following formula: risk score = (βNSRlncRNA1 × expNSRlncRNA1) + (βNSRlncRNA2 × expN-SRlncRNA2) + ••• + (βNSRlncRNAn × expNSRlncRNAn).The βNSRlncRNA referred to the relevant coefficient of NSRlncRNA by LASSO analysis, and expNSRlncRNA referred to the expression of NSRlncRNA.Based on the median risk score, ccRCC patients would be classified into high-and low-risk groups.Principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) were performed for the analysis of reducing dimension in exploring the power of clustering ability about the prognostic signature.K-M curves and time-dependent receiver operating characteristic (ROC) curves were conducted to evaluate the predictive power of our constructed signature in prognosis.

Construction of the nomogram
First, we explored the correlation between the prognostic signature and clinical variables (age, gender, AJCC stage, and grade).Second, we performed univariate and multivariate Cox regression analyses for investigating whether the prognostic signature was of independent prognostic value in patients with ccRCC.Finally, based on the result of multivariate COX regression analysis, a nomogram consisting of several clinicopathological variables and risk score was established.We drew the nomogram to forecast the 1-, 3-, and 5-year survival probabilities of

Functional enrichment analysis
Gene set enrichment analysis (GSEA) was conducted to uncover the underlying functional pathways of the Notch-correlated lncRNA prognostic signature via performing GSEA software (Version 4.1.0),and FDR < 0.25 represented significantly enriched pathways.Additionally, the differentially expressed genes (DEGs) were identified among two risk groups with the criterion of FDR < 0.05 and |log FC |> 1. Subsequently, we performed the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes [28][29][30] (KEGG) analyses to explore the potential cancer biology based on the results of the differentially expressed analysis with the criterion of p < 0.05.

Identification of candidate small molecule drugs
The differentially expressed genes based on the NSRlncRNA-based signature were classified into upregulated gene group and downregulated gene group.Subsequently, these genes were separately submitted into the L1000FWD website (https:// maaya nlab.cloud/ L1000 FWD/), and the corresponding results were acquired.

Drug sensitivity prediction
The Genomics of Drug Sensitivity in Cancer (GDSC, http:// www.cance rrxge ne.org) database was conducted to predict the response to common drugs of ccRCC patients in two groups.The sensitivity was assessed via calculating the half-maximal inhibitory concentration (IC50) using R package "pRRophetic".P-value < 0.05 was deemed as statistical criteria..

Consensus Clustering
In the total TCGA cohort, based on the expression levels of risk score, hierarchical clustering was conducted by using the R package "ConsensusClusterPlus".PCA and t-SNE analyses were conducted to further confirm the clustering power.Furthermore, we also explored the difference of survival outcomes, immune microenvironment, expression of immune checkpoint, and immune cell infiltration among the clusters.

Statistical analysis
All statistical analyses were conducted based on R software (Version 4.0.5).P < 0.05 was served as statistical criteria.

Identification of the key Notch signaling-related lncRNA
In the TCGA dataset, a total of 1422 lncRNAs showed the significant correlation with 32 Notch signaling genes and were defined as NSRlncRNA.The relevant co-expression network between lncRNA and Notch signaling genes was shown in Fig. 2. Subsequently, in order to discovery the vital module of NSRlncRNA in ccRCC, WGCNA analysis was conducted in the TCGA dataset, and the optimal threshold value was 10 if the correlational coefficient was > 0.9 (Fig. 3A).These NSRlncRNAs were classified by the average linkage hierarchical clustering approach, and three modules were generated (Fig. 3B).Furthermore, the blue module containing 88 NSRlncRNAs was negatively correlated with ccRCC tissues (r = − 0.68, p = 1e-83) and used for further analysis (Fig. 3C).Meanwhile, 182 DENSRlncRNAs containing 119 upregulated NSRlncRNAs and 63 downregulated NSRlncRNAs, were identified between ccRCC samples and normal samples (Fig. 3D).Finally, The Venn diagram was performed to obtain the 41 key NSRlncRNAs (Fig. 3E), namely the intersection of the result of WGCNA analysis and DENSRlncRNAs, which were showed by the heatmap (Fig. 3F).

Establishment of the prognostic signature
Univariate Cox regression analysis was applied to screen the prognostic lncRNA from 41 key NSRlncRNAs in the training cohort, and 13 prognostic NSRlncRNAs were identified (p < 0.05) (Fig. 4A).To avert the risk of over-fitting, these NSRlncRNAs were incorporated into LASSO regression analysis for developing the predictive signature (Fig. 4B,C), of which five NSRlncRNAs were utilized to establish the signature.The risk score = (-0.002× AC092611.2expression) + (− 0.0359 × NNT-AS1 expression) + (0.0395 × AGAP2-AS1 expression) + (0.0656 × AC147651.3expression) + (− 0.0243 × AC007406.3expression).Based on the median risk score generated via the previous formula, 266 patients in the training cohort were clustered into two different groups (high-risk group and low-risk group).PCA and t-SNE analyses displayed the excellent clustering power of NSRlncRNA-based risk score (Fig. 5A,B).Kaplan-Meier curves showed the much longer overall survival of ccRCC patients in the low-risk group compared with the high-risk group (p = 0.001; Fig. 5C), with the 1-, 3-, and 5-year AUC values of 0.762, 0.655, and 0.661, respectively (Fig. 5D).The distribution and survival status of ccRCC patients stratified by the median risk score were shown in the Fig. 6B.The expression patterns of 5 NSRlncRNAs

Construction of a nomogram
To validate the clinical value of the signature, we explored whether risk score could be served as an independent prognostic index in ccRCC.The clinical variables (age, sex, grade, and AJCC stage) and risk score were incorporated for the univariate and multivariate COX regression analysis, and the results indicated that risk score was an independent prognostic index for patients with ccRCC in the training cohort, testing cohort, and total cohort (Fig. 7).To facilitate the clinical application of the risk score in prognosis, a nomogram was established (Fig. 8A).Additionally, both calibration curves and DCA curves showed the favorable performance of nomogram in predicting prognosis for patients with ccRCC (Fig. 8B-E).Moreover, we also explored the correlation between risk score and clinical variables, and discovered that the risk score was closely related to advanced clinical variables and significantly elevated in subgroups of grade (G3 and G4), male, and AJCC stage (Stage III and Stage IV) (Supplementary Fig. 1).

Immune cell infiltration
Given the significance of immune microenvironment and immune checkpoint inhibitor therapy in ccRCC, we explored the relationship between immune microenvironment and the prognostic signature by CIBERSORT, CIBERSORT-ABS, ESTIMATE, EPIC, MCP counter, TIMER, and XCELL algorithms, and the relevant results were shown in Fig. 9B.The results of ESTIMATE analysis suggested the obvious difference of the immune score, stromal score, and ESTIMATE score among the high-and low-risk groups (Fig. 9A).Moreover, the expression of several immune checkpoints, such as PDCD1, LAG3, CD274, HAVCR2, LAG3, and TIGIT was distinctly different among two groups (Supplementary Fig. 2).All these results suggested that NSRlncRNA-based signature

Functional enrichment analysis
Considering the correlation between the NSRlncRNA-based signature and prognosis as well as clinical variables, we further explored the biological functional mechanisms among high-and low-risk groups.GSEA suggested that NSRlncRNA-based signature was notably associated with mTOR signaling pathway, ERBB signaling pathway, the regulation of autophagy, insulin signaling pathway, and peroxisome, fatty acid metabolism, propanoate metabolism, and sphingolipid metabolism (Supplementary Fig. 3A).The results of KEGG [28][29][30] analysis indicated that DEGs among two groups were mainly associated with cytokine-cytokine receptor interaction, ECM − receptor interaction, PI3K-Akt and IL-17 signaling pathways, complement and coagulation cascades, and NF − kB signaling pathway (Supplementary Fig. 3C).The results of GO analysis indicated that DEGs among two groups were mainly implicated in the extracellular matrix organization, extracellular structure organization, collagen metabolic process, chemokine activity, basement membrane, peptidase regulator activity, and so on (Supplementary Fig. 3B).

Identification of the candidate drug
To further identify the candidate therapeutic drugs for ccRCC, we submitted the upregulated genes and downregulated genes among high-and low-risk groups into the L1000FWD database.The top ten significant candidate compounds were served as the therapeutic drugs for the treatment of ccRCC and might enhance the anticancer effect.(Table 1).In addition, we found that these drugs were mainly enriched in the dopamine receptor antagonist, FLT3 inhibitor/JAK inhibitor, EGFR inhibitor, PI3K inhibitor, glycogen synthase kinase inhibitor, and NF-kB pathway inhibitor, which might provide novel directions for developing targeted drugs.

Drug sensitivity prediction
Figure 10 uncovered the results of some commonly targeted or chemotherapy drugs for ccRCC.Our results revealed that the IC50 levels of Gefitinib, Sorafenib, Sunitinib, Temsirolimus, Erlotinib, Cisplatin, Gemcitabine, www.nature.com/scientificreports/Docetaxel, and Rapamycin in the high-risk group were much lower than those in the low-risk group, suggesting that patients with ccRCC in the high-risk group might benefit from the treatment of these drugs, while the IC50 levels of Lapatinib in high-risk group were much higher than that in the low-risk group, suggesting that patients with ccRCC in the low-risk group might benefit from the treatment of Lapatinib.Nevertheless, there were no obvious differences in the sensitivity of Axitinib and Pazopanib among two groups.

Consensus clustering
To develop a classification of ccRCC based on the expression level of risk score, unsupervised consensus clustering was performed on ccRCC patients in the total cohort.We found that k = 4 had the optimum clustering power, and patients with ccRCC were separated into four subgroups: Cluster 1 (n = 318), Cluster 2 (n = 110), Cluster 3 (n = 84), and Cluster 4 (n = 18) (Fig. 11A-C).PCA and t-SNE analyses also displayed the stability of clustering power between four subgroups (Fig. 11D,E).The results of Kaplan-Meier analysis showed that the overall survival of Cluster 1 and Cluster 3 were the best, followed by the Cluster 2, and the Cluster 4 being the worst (Fig. 11F).
In addition, CD4 + T cells, T cell follicular helper, Tregs, macrophage M1 and M2, and neutrophil were the obvious immune infiltrating cells between these four clusters (Fig. 12C).The expression levels of LAG3, PD-1, and CTLA4 in cluster 2 and cluster 4 were much higher compared than those in cluster 1 and cluster 3 (Fig. 12B).
The expression levels of HAVCR2 and PD-L1 in cluster 1 and cluster 3 were markedly higher than that in cluster 2, and cluster 4 being the lowest.The immune score in cluster 2 was the highest, the cluster 1, cluster3, and
In addition, renal cell carcinoma is one of the most immune-infiltrated tumors 42,43 .Emerging evidence suggests that the activation of specific metabolic pathway have a role in regulating angiogenesis and inflammatory signatures 44,45 .Features of the tumor microenvironment heavily affect disease biology and may affect responses to systemic therapy [46][47][48][49] .Notch signaling can modulate immune cell infiltration and regulate immunoflogosis 50 .
To the best of my knowledge, lncRNA has been claimed to function as an important role in tumorigenesis, tumor progression, metastasis, and drug resistance.In the present study, a total of 41 NSRlncRNAs were found to might be significantly correlated with tumorigenesis by integrated bioinformatic analyses including WGCNA, of which 13 NSRlncRNAs were identified to be associated with prognosis by univariate Cox analysis.Subsequently, LASSO regression analysis was conducted to establish a Notch-related lncRNA signature comprising of five lncRNAs (AC092611.2,NNT-AS1, AGAP2-AS1, AC147651.3,and AC007406.3),which displayed the favorable predictive value for prognosis in the training cohort, testing cohort, and entre cohort.ROC analyses also confirmed the feasibility and accuracy of our lncRNA-based signature.Furthermore, multivariate Cox analysis indicated that the lncRNA-based signature was an independent prognostic indicator.The risk score and other clinical variables (age, AJCC stage, and grade) were incorporated to construct a nomogram for predicting the outcomes and guiding decision-making.
In the current signature consisting of five NSRlncRNAs (AC092611.2,NNT-AS1, AGAP2-AS1, AC147651.3,and AC007406.3),several NSRlncRNAs have been claimed to be involved in the carcinogenesis including ccRCC, or have an influence on the prognosis of patients with malignant renal tumor.Among the five NSRlncRNAs, AC092611.2and AC147651.3were identified as autophagy-and immune-related lncRNAs in ccRCC by bioinformatic analysis, respectively 53,54 .Nevertheless, the biological function of AC007406.3 in tumor has never been explored.Lnc RNA NNT-AS1 functioned as a cancer promotor in various tumors.For example, NNT-AS1 has been shown to function as the oncogene role in cancer, which was elevated in non-small cell lung cancer (NSCLC) and tightly correlated with the tumorigenic phenotypes and unfavorable prognosis 55 .NNT-AS1 was obviously upregulated in ccRCC samples and NNT-AS1 advanced the proliferation, invasion, and metastasis of  ccRCC cells via sponging miR-137 to especially elevate the expression of YBX-1 56 .A meta-analysis suggested that the overexpression of NNT-AS1 was correlated with poor prognosis and advanced clinicopathologic characteristics in tumors 57 .AGAP2-AS1, a known cancer-related lncRNA activated by SP1 and RREB1, has been broadly upregulated in various types of tumors and correlated with the biological behaviors of cancer cells, including lung cancer 58,59 , breast cancer, gastric cancer 60 , pancreatic cancer 61 , hepatocellular cancer, esophageal cancer 62 , and ccRCC 63 .
The crucial role of immune infiltration in the tumorigenesis, progression, prognosis, and drug resistance of ccRCC has been broadly acknowledged.Infiltrated CD4 + T cells stimulated the proliferation of RCC cells via regulating TGFβ1/YBX1/HIF2α pathway 64 .The cytotoxic CD8 + T cells could attenuate the growth of cancer cells by targeting antigenic cancer cells 65 .Elevated peritumoral Tregs was associated with inferior outcomes in ccRCC 66 .A meta-analysis showed that the higher infiltration of FoxP3 + Tregs was notably correlated with shorter prognosis in several solid tumors 67 .We first explored the relationship between the lncRNA-based prognostic signature and tumor microenvironment.Our results suggested that lncRNA-based signature displayed the positive correlation with stromal score, immune score, and ESTIMATE score.Furthermore, we also found the obvious differences of the abundances of various immune cell between high-risk group and low-risk group, which implied that the Notch-related lncRNA signature was correlated with immune infiltration.Targeted therapy and immunotherapy were crucial for the treatments of advanced or metastatic ccRCC owing to the unsensitive to radiotherapy and chemotherapy.Hence, we further investigated the response to immunotherapy and targeted drugs among two groups.The results suggested that ccRCC patients in high-risk group were much more sensitive to targeted drugs, including Sunitinib, Sorafenib, Gefitinib, Erlotinib, Pazopanib, Lapatinib, Rapamycin, and Temsirolimus.Moreover, immune checkpoint inhibitors immunotherapy, an emerging antitumor therapy, has been authorized for various malignancies.In our study, the expressions of PD-L1, HAVCR2, PD-1, LAG-3, TIGIT, and CTLA-4 were much more markedly elevated in the high-risk group than those in the low-risk group, suggesting that patients in the high-risk group might be suitable for immunotherapy.All these results uncovered that our constructed prognostic signature might optimize clinical therapeutic options and serve as potential biomarker.
Of course, several disadvantages to our study also should be acknowledged.First, the development and verification of the prognostic model were mainly depended on only one retrospective cohort from TCGA dataset, and much more prospective data for verification should be essential.Additionally, all the results of our research were acquired via bioinformatic methods, which should be further experimental validation.
In conclusion, we identified a robust prognostic signature containing five Notch signaling-related lncRNAs.Moreover, this prognostic signature was of great clinical power for evaluating tumor immune microenvironment and providing individualized therapeutic strategies in patients with ccRCC.

Figure 1 .
Figure 1.The flow chart of the present study.

Figure 2 .
Figure 2. The network of the correlations between Notch signaling-related genes and lncRNA.

Figure 3 .
Figure 3. Identification of the key Notch-related lncRNA.(A) The network topographies with various soft threshold powers; (B) Dendrogram of Notch-related lncRNA clustered on the basis of a dissimilarity measure; (C) The correlation between the clinical traits and Notch-related lncRNA; (D) Volcano map for the differentially expressed Notch-related lncRNA; (E) Venn diagram for identification of the key Notch-related lncRNA; (F) Heatmap showed the key Notch-related lncRNA between normal samples and tumor samples.

Figure 4 .
Figure 4. Identification of the prognostic Notch-related lncRNA.(A) Identification of the prognostic lncRNA by univariate Cox regression analysis (P < 0.05); (B) Tenfold cross-validation for tuning in the LASSO analysis; (C) The coefficient profile of five by LASSO regression analysis.

Figure 5 .
Figure 5. Construction and validation of the Notch-related lncRNA signature.PCA analysis in the training TCGA dataset (A), testing TCGA dataset (E), and total TCGA dataset (I); t-SNE analysis in the training TCGA dataset (B), testing TCGA dataset (F), and total TCGA dataset (J); Kaplan-Meier curve indicated that the low-risk patients had better prognosis compared with the high-risk patients in the training TCGA dataset (C), testing TCGA dataset (G), and total TCGA dataset (K); Time-independent receiver operating characteristic (ROC) analysis for evaluating the predictive ability of Notch-related lncRNA signature in the training TCGA dataset (D), testing TCGA dataset (H), and total TCGA dataset (L).

Figure 6 .
Figure 6.Heatmap showed the expression of prognostic Notch-related lncRNA between the high-risk group and low-risk group in the training TCGA dataset (A), testing TCGA dataset (C), and total TCGA dataset (E); Distribution of risk scores and survival status for the ccRCC patients based on the Notch-related lncRNA signature in the training TCGA dataset (B), testing TCGA dataset (D), and total TCGA dataset (F).
Recently, accumulating studies aimed to explore the value of lncRNA functioning as noninvasive biological markers for diagnosis, recurrence, prognosis, and forecasting the therapeutic effects.Furthermore, the lncRNA-stratified prognostic signature has been extensively used for predicting the prognosis and therapeutic response of patients with cancer, including ccRCC.Sun et al. constructed and verified a novel signature stratified by five immune-related lncRNA 51 , exhibiting the excellent capacity in survival prediction of patients with ccRCC.Dong et al. established a nine redox-related lncRNA signature correlated with overall survival in ccRCC.Additionally, Yu et al. identified novel lncRNA-based immune index for predicting the response to immune checkpoint inhibitor immunotherapy 52 .

Figure 7 .
Figure 7.The Notch-related lncRNA signature was an independent prognostic index for patients with ccRCC.The univariate Cox analysis for evaluating the prognostic value of Notch-related lncRNA signature in the training TCGA dataset (A), testing TCGA dataset (D), and total TCGA dataset (G); (B) Multivariate Cox analysis for evaluating the independent prognostic value of Notch-related lncRNA signature in the training TCGA dataset (B), testing TCGA dataset (E), and total TCGA dataset (H); (C) ROC curves analyses of the clinical variables and risk score in the training TCGA dataset (C), testing TCGA dataset (F), and total TCGA dataset (I).

Figure 8 .
Figure 8. Establishment of the nomogram in the total TCGA dataset.(A) Nomogram based on the risk score and clinical variables for predicting 1-year, 3-year, and 5-year survival between ccRCC patients; (B) The calibration curves of the nomogram showed the concordance between predicted and actual 1-, 3-, and 5-year survival; (C-E) Decision curve analysis for evaluating the clinical utility of the nomogram in 1-year, 3-year, and 5-year, respectively.

Figure 9 .
Figure 9.The relationship between the Notch-related lncRNA signature and tumor microenvironment.(A) Boxplot showed the expression of ESTIMATE Score, Immune Score, Stromal Score among high-risk group and low-risk group via the ESTIMATE algorithm; (B) Heatmap showed the immune cells infiltration among highrisk group and low-risk group by CIBERSORT, QUANTISEQ, MCPcounter, XCELL, CIBERSORT-ABS, TIMER and EPIC algorithms.

Figure 12 .
Figure 12.The immune characteristics of the prognostic lncRNA patterns between four clusters.(A) The expression of immune score, stromal cell score, and ESTIMATE Score among four clusters; (B) The expression of key immune checkpoints among four clusters; (C) The distribution of 22 immune cells infiltration among four clusters.

Table 1 .
Identification of the top 10 candidate drugs by using L1000FWD database.